motorola motorola edge 40 neo
Geekbench ML Score
279
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 25 2024 09:31 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | motorola motorola edge 40 neo |
| Model ID | motorola motorola edge 40 neo |
| Motherboard | manaus |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.50 GHz |
| Memory Information | |
|---|---|
| Size | 11.19 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
144
26.9 IPS |
|
|
Image Classification (F16)
|
100% |
229
42.8 IPS |
|
|
Image Classification (I8)
|
97% |
197
36.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
199
3.33 IPS |
|
|
Image Segmentation (F16)
|
100% |
288
4.81 IPS |
|
|
Image Segmentation (I8)
|
98% |
289
4.83 IPS |
|
|
Pose Estimation (F32)
|
100% |
161
0.20 IPS |
|
|
Pose Estimation (F16)
|
100% |
255
0.31 IPS |
|
|
Pose Estimation (I8)
|
100% |
254
0.31 IPS |
|
|
Object Detection (F32)
|
100% |
130
9.72 IPS |
|
|
Object Detection (F16)
|
100% |
199
14.8 IPS |
|
|
Object Detection (I8)
|
62% |
190
14.2 IPS |
|
|
Face Detection (F32)
|
100% |
344
4.09 IPS |
|
|
Face Detection (F16)
|
98% |
510
6.07 IPS |
|
|
Face Detection (I8)
|
86% |
482
5.73 IPS |
|
|
Depth Estimation (F32)
|
100% |
325
2.52 IPS |
|
|
Depth Estimation (F16)
|
100% |
397
3.08 IPS |
|
|
Depth Estimation (I8)
|
95% |
397
3.08 IPS |
|
|
Style Transfer (F32)
|
100% |
721
0.95 IPS |
|
|
Style Transfer (F16)
|
100% |
994
1.31 IPS |
|
|
Style Transfer (I8)
|
98% |
995
1.31 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
151
5.38 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
207
7.39 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
208
7.43 IPS |
|
|
Text Classification (F32)
|
100% |
160
230.0 IPS |
|
|
Text Classification (F16)
|
100% |
165
236.4 IPS |
|
|
Text Classification (I8)
|
92% |
333
478.5 IPS |
|
|
Machine Translation (F32)
|
100% |
293
5.40 IPS |
|
|
Machine Translation (F16)
|
100% |
295
5.42 IPS |
|
|
Machine Translation (I8)
|
64% |
295
5.42 IPS |
